{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Everything ready.\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "print('Everything ready.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "Yes\n",
    "Yes\n",
    "No\n",
    "No\n",
    "Yes\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 30.986498 seconds\n",
      "One loop version took 55.105681 seconds\n",
      "No loop version took 0.337175 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.241000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.243000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.264000\n",
      "k = 5, accuracy = 0.256000\n",
      "k = 5, accuracy = 0.271000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.289000\n",
      "k = 5, accuracy = 0.278000\n",
      "k = 8, accuracy = 0.263000\n",
      "k = 8, accuracy = 0.287000\n",
      "k = 8, accuracy = 0.276000\n",
      "k = 8, accuracy = 0.288000\n",
      "k = 8, accuracy = 0.270000\n",
      "k = 10, accuracy = 0.266000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.279000\n",
      "k = 10, accuracy = 0.283000\n",
      "k = 10, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.261000\n",
      "k = 12, accuracy = 0.294000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.253000\n",
      "k = 15, accuracy = 0.290000\n",
      "k = 15, accuracy = 0.279000\n",
      "k = 15, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.275000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.280000\n",
      "k = 20, accuracy = 0.284000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = []\n",
    "    for i in range(num_folds):\n",
    "        # train\n",
    "        X_valid = X_train_folds.pop(0)\n",
    "        y_valid = y_train_folds.pop(0)\n",
    "        X_train_set = np.concatenate(X_train_folds)\n",
    "        y_train_set = np.concatenate(y_train_folds)\n",
    "        classifier.train(X_train_set, y_train_set)\n",
    "        # predict\n",
    "        y_valid_pred = classifier.predict(X_valid, k=k)\n",
    "        num_correct = np.sum(y_valid_pred == y_valid)\n",
    "        k_to_accuracies[k].append(float(num_correct)/y_valid.shape[0])\n",
    "        # prepare for next train\n",
    "        X_train_folds.append(X_valid)\n",
    "        y_train_folds.append(y_valid)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
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   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "2, 4\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  }
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